research-article

Efficient community detection in large networks using content and links

Authors:

Srinivasan ParthasarathyAuthors Info & Claims

WWW '13: Proceedings of the 22nd international conference on World Wide Web

Pages 1089 - 1098

https://doi.org/10.1145/2488388.2488483

Published: 13 May 2013 Publication History

Abstract

In this paper we discuss a very simple approach of combining content and link information in graph structures for the purpose of community discovery, a fundamental task in network analysis. Our approach hinges on the basic intuition that many networks contain noise in the link structure and that content information can help strengthen the community signal. This enables ones to eliminate the impact of noise (false positives and false negatives), which is particularly prevalent in online social networks and Web-scale information networks.

Specifically we introduce a measure of signal strength between two nodes in the network by fusing their link strength with content similarity. Link strength is estimated based on whether the link is likely (with high probability) to reside within a community. Content similarity is estimated through cosine similarity or Jaccard coefficient.

We discuss a simple mechanism for fusing content and link similarity. We then present a biased edge sampling procedure which retains edges that are locally relevant for each graph node. The resulting backbone graph can be clustered using standard community discovery algorithms such as Metis and Markov clustering.

Through extensive experiments on multiple real-world datasets (Flickr, Wikipedia and CiteSeer) with varying sizes and characteristics, we demonstrate the effectiveness and efficiency of our methods over state-of-the-art learning and mining approaches several of which also attempt to combine link and content analysis for the purposes of community discovery. Specifically we always find a qualitative benefit when combining content with link analysis. Additionally our biased graph sampling approach realizes a quantitative benefit in that it is typically several orders of magnitude faster than competing approaches.

References

[1]

C. Aggarwal, Y. Zhao, and P. Yu. Outlier detection in graph streams. In Data Engineering (ICDE), 2011 IEEE 27th International Conference on, pages 399--409. IEEE, 2011.

Digital Library

[2]

D. Blei, A. Ng, and M. Jordan. Latent dirichlet allocation. JMLR, 3:993--1022, 2003.

Digital Library

[3]

A. Broder, M. Charikar, A. Frieze, and M. Mitzenmacher. Min-wise independent permutations. In STOC?98, pages 327--336. ACM, 1998.

Digital Library

[4]

M. Charikar. Similarity estimation techniques from rounding algorithms. In STOC'02, pages 380--388. ACM, 2002.

Digital Library

[5]

D. Cohn and T. Hofmann. The missing link-a probabilistic model of document content and hypertext connectivity. In NIPS'01, volume 13, page 430. The MIT Press, 2001.

[6]

I. Dhillon, Y. Guan, and B. Kulis. Kernel k-means: spectral clustering and normalized cuts. In SIGKDD'04, pages 551--556. ACM, 2004.

Digital Library

[7]

E. Erosheva, S. Fienberg, and J. Lafferty. Mixed-membership models of scientific publications. PNAS, 101(Suppl 1):5220, 2004.

[8]

M. Ester, R. Ge, B. Gao, Z. Hu, and B. Ben-Moshe. Joint cluster analysis of attribute data and relationship data: the connected k-center problem. In SDM'06, pages 25--46, 2006.

[9]

S. Fortunato. Community detection in graphs. Physics Reports, 486(3-5):75--174, 2010.

[10]

E. Fox and J. Shaw. Combination of multiple searches. NIST SPECIAL PUBLICATION SP, pages 243--243, 1994.

[11]

T. Griffiths and M. Steyvers. Finding scientific topics. PNAS, 101(Suppl 1):5228, 2004.

[12]

S. Günnemann, B. Boden, and T. Seidl. Db-csc: a density-based approach for subspace clustering in graphs with feature vectors. In PKDD 2011, pages 565--580. Springer-Verlag, 2011.

Digital Library

[13]

D. Hanisch, A. Zien, R. Zimmer, and T. Lengauer. Co-clustering of biological networks and gene expression data. Bioinformatics, 18(suppl 1):S145--S154, 2002.

[14]

D. Karger. Random sampling in cut, flow, and network design problems. Mathematics of Operations Research, 24(2):383--413, 1999.

[15]

G. Karypis and V. Kumar. A fast and high quality multilevel scheme for partitioning irregular graphs. SIAM Journal on Scientific Computing, 20(1):359--392, 1998.

Digital Library

[16]

J. Leskovec, K. Lang, and M. Mahoney. Empirical comparison of algorithms for network community detection. In Proceedings of the 19th international conference on World wide web, pages 631--640. ACM, 2010.

Digital Library

[17]

A. Maiya and T. Berger-Wolf. Sampling community structure. In WWW 2010, pages 701--710. ACM, 2010.

Digital Library

[18]

B. Mohar. The laplacian spectrum of graphs. Graph theory, combinatorics, and applications, 2:871--898, 1991.

[19]

M. Montague and J. Aslam. Relevance score normalization for metasearch. In CIKM'01, pages 427--433. ACM, 2001.

Digital Library

[20]

R. Nallapati, A. Ahmed, E. Xing, and W. Cohen. Joint latent topic models for text and citations. In SIGKDD'08, pages 542--550. ACM, 2008.

Digital Library

[21]

V. Satuluri and S. Parthasarathy. Scalable graph clustering using stochastic flows: applications to community discovery. In SIGKDD'09, pages 737--746. ACM, 2009.

Digital Library

[22]

V. Satuluri, S. Parthasarathy, and Y. Ruan. Local graph sparsification for scalable clustering. In SIGMOD 2011, pages 721--732. ACM, 2011.

Digital Library

[23]

A. Strehl and J. Ghosh. Cluster ensembles?a knowledge reuse framework for combining multiple partitions. The Journal of Machine Learning Research, 3:583--617, 2003.

Digital Library

[24]

W. Tang, Z. Lu, and I. Dhillon. Clustering with multiple graphs. In ICDM'09, pages 1016--1021. IEEE, 2009.

Digital Library

[25]

Y. Tian, R. A. Hankins, and J. M. Patel. Efficient aggregation for graph summarization. In SIGMOD'08, pages 567--580, 2008.

Digital Library

[26]

I. Ulitsky and R. Shamir. Identification of functional modules using network topology and high-throughput data. BMC Systems Biology, 1(1):8, 2007.

[27]

S. van Dongen. Graph clustering by flow simulation. PhD Thesis, 2000.

[28]

T. Yang, R. Jin, Y. Chi, and S. Zhu. Combining link and content for community detection: a discriminative approach. In SIGKDD'09, pages 927--936. ACM, 2009.

Digital Library

[29]

D. Zhou, E. Manavoglu, J. Li, C. Giles, and H. Zha. Probabilistic models for discovering e-communities. In WWW'06, pages 173--182. ACM, 2006.

Digital Library

[30]

Y. Zhou, H. Cheng, and J. Yu. Clustering large attributed graphs: An efficient incremental approach. In ICDM 2010, pages 689--698. IEEE, 2010.

Digital Library

[31]

S. Zhu, K. Yu, Y. Chi, and Y. Gong. Combining content and link for classification using matrix factorization. In SIGIR'07, pages 487--494. ACM, 2007.

Digital Library

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https://doi.org/10.1109/TNSE.2024.3524077
Singh SSrivastava DVerma MMuhuri S(2025)Information diffusion analysis: process, model, deployment, and applicationThe Knowledge Engineering Review10.1017/S026988892400010939Online publication date: 22-Jan-2025
https://doi.org/10.1017/S0269888924000109
Dabaghi-Zarandi FAfkhami MAshoori M(2025)Community Detection method based on Random walk and Multi objective Evolutionary algorithm in complex networksJournal of Network and Computer Applications10.1016/j.jnca.2024.104070234(104070)Online publication date: Feb-2025
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Index Terms

Efficient community detection in large networks using content and links
1. Information systems
  1. Information systems applications
    1. Data mining

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Published In

cover image ACM Other conferences

WWW '13: Proceedings of the 22nd international conference on World Wide Web

May 2013

1628 pages

ISBN:9781450320351

DOI:10.1145/2488388

General Chairs:
Daniel Schwabe
PUC-Rio - Brazil
,
Virgílio Almeida
UFMG - Brazil
,
Hartmut Glaser
CGI.br - Brazil
,
Program Chairs:
Ricardo Baeza-Yates
Yahoo! Labs - Spain & Chile
,
Sue Moon
KAIST - South Korea

Copyright © 2013 Copyright is held by the International World Wide Web Conference Committee (IW3C2).

Sponsors

NICBR: Nucleo de Informatcao e Coordenacao do Ponto BR
CGIBR: Comite Gestor da Internet no Brazil

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SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 13 May 2013

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WWW '13: 22nd International World Wide Web Conference

May 13 - 17, 2013

Rio de Janeiro, Brazil

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WWW '13 Paper Acceptance Rate 125 of 831 submissions, 15%;

Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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https://doi.org/10.1109/TNSE.2024.3524077
Singh SSrivastava DVerma MMuhuri S(2025)Information diffusion analysis: process, model, deployment, and applicationThe Knowledge Engineering Review10.1017/S026988892400010939Online publication date: 22-Jan-2025
https://doi.org/10.1017/S0269888924000109
Dabaghi-Zarandi FAfkhami MAshoori M(2025)Community Detection method based on Random walk and Multi objective Evolutionary algorithm in complex networksJournal of Network and Computer Applications10.1016/j.jnca.2024.104070234(104070)Online publication date: Feb-2025
https://doi.org/10.1016/j.jnca.2024.104070
Kato TMiyakoshi JMatsumura TMine RMizuno HDeguchi Y(2024)Mixbiotic society measures: Assessment of community well-going as living systemPLOS ONE10.1371/journal.pone.030740119:8(e0307401)Online publication date: 7-Aug-2024
https://doi.org/10.1371/journal.pone.0307401
Berahmand KBahadori SAbadeh MLi YXu Y(2024)SDAC-DA: Semi-Supervised Deep Attributed Clustering Using Dual AutoencoderIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.338904936:11(6989-7002)Online publication date: Nov-2024
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Lee JJeong DJung EKim YKim JHe YChoi S(2024)Mapping the Evolutionary Pattern of Mobile Products: A Phylogenetic ApproachIEEE Transactions on Engineering Management10.1109/TEM.2022.321448971(4776-4790)Online publication date: 2024
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Sun XWang WHuang T(2024)Efficient Multi-network Community Search Method for Distributed Graph Iterative Computation2024 IEEE 48th Annual Computers, Software, and Applications Conference (COMPSAC)10.1109/COMPSAC61105.2024.00050(306-311)Online publication date: 2-Jul-2024
https://doi.org/10.1109/COMPSAC61105.2024.00050
Mohammadi ASeyedi SAkhlaghian Tab FPir Mohammadiani R(2024)Diverse joint nonnegative matrix tri-factorization for attributed graph clusteringApplied Soft Computing10.1016/j.asoc.2024.112012164(112012)Online publication date: Oct-2024
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Shang RWang SZhang WFeng JJiao LStolkin R(2024)Evolutionary multi-objective overlapping community detection based on fusion of internal and external connectivity and correction of node intimacyApplied Soft Computing10.1016/j.asoc.2024.111414(111414)Online publication date: Feb-2024
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Yan CMa HTang YLi Z(2024)Attribute subspace-guided multi-scale community detectionNeural Computing and Applications10.1007/s00521-024-09751-636:22(13975-13988)Online publication date: 2-May-2024
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